Nasscom, India's largest IT industry trade body, has released "Generative Engine Optimization (GEO) for Academic Journals: Preparing Scholarly Content for AI-Powered Research Discovery." The publication is among the first major trade-body treatments of GEO aimed at scholarly publishers rather than consumer marketers, and it lands as a growing share of working scientists begin their literature searches inside AI assistants.

Where SEO (search engine optimization) targeted ranked web pages, GEO targets the citation behavior of large language models (LLMs), AI Overviews, and dedicated research tools such as Elicit, Consensus, and Scite. The acronym echoes SEO deliberately, signaling an extension rather than a replacement.

What does GEO demand of an academic journal?

The shift reframes who the audience is. For two decades, journal publishers optimized for Google Scholar rankings, DOI (Digital Object Identifier) indexing, inclusion in abstract-and-index services like Scopus and Web of Science, and journal-level authority signals such as impact factor (a citation-based measure of journal prestige). The Nasscom document argues those signals are no longer sufficient.

Researchers now open ChatGPT, Claude, Gemini, or Perplexity before they open a database. They ask for summaries, methodological comparisons, and citation recommendations. The answers they receive draw on content that those models can retrieve, parse, and cite cleanly. Articles that lack clean structure, machine-readable metadata, or accessible full text get skipped.

The guidance implies several practical adjustments for journal publishers:

• Structured abstracts and clear thesis statements. AI systems extract claims that read well in isolation. Burying the finding under long literature-review passages weakens extractability.
• Standardized metadata. ORCID identifiers (persistent digital IDs for individual researchers), dataset DOIs, and schema.org markup help retrieval systems attribute and parse scholarly work.
• Open access pathways. Green open access (the author-deposited, accepted manuscript version) keeps content visible to AI crawlers even when the version of record is paywalled.
• Author and institution prominence. Models prefer to cite named experts from identifiable institutions, rewarding individual branding over anonymous journal pages.

Why Nasscom, and why now?

The trade body represents India's IT services and product companies, many of which now build enterprise GEO tools for marketing teams. Turning the same lens on academic publishing extends a practice already in commercial deployment and gives journal publishers a framework written from outside the traditional STM (Scientific, Technical, and Medical) sector.

What should publishers monitor next?

Three signals will indicate whether GEO has moved from advisory to operational:

1. Major abstract-and-index services such as Scopus, Web of Science, and Dimensions add GEO-specific quality scores or fields to journal records.
2. Publishers begin reporting "AI citation" or "AI referral" traffic in their annual reports alongside Google Scholar metrics.
3. Peer review processes adopt structured GEO checks, similar to how structured abstracts were standardized in earlier decades.

Until those signals appear, the Nasscom document reads as a strategic preview rather than a settled playbook. Publishers that treat it as one will buy themselves time to test, measure, and adapt before the metrics themselves catch up.